Frame: we are thinkTank approached by policy makers from the immigration office with the task of informing how immigration was discussed in the HoC. - covid, uk entering hardship, immigration will become a sensitive topic, they want to be prepared. - they want to get what the uk relation to immigration is, in terms of intensity of discussions and in terms of sentiment -understanding topics and sentiment is important for coalition building - they have quantities data, but wish to understand better the discourse. for that, we came with the following guiding hypothesis: 1. 2. 3.
It’s 1978. Margaret Thatcher takes an interview in the notorious British programme World in Action in which she voices, what in her view, is the recent sentiment of The People regarding immigration. Remarkedly, she notes that “People are really rather afraid that this country might be rather swamped by people with a different culture.” Jumping in time. It’s 2010, David Cameroon wins BY A LOT the general election but nevertheless could not form a coalition, resulting eventually in broad coalition government. It’s 2015 now, an exceptional amount of asylum seekers make their way to European Union (EU), and the United Kingdom (UK) is facing many applications for asylum, double the amount it did just one year before (Riihimäki, 2016). A year ahead, the date is June 23, 2016 and the British decide to withdraw from the EU and make the UK GREAT AGAIN. All in all, things haven’t been easy for the UK.
Knowing that in part these described transformations were driven by discussions about “people with a different culture” elicits the need to understand how these people are discussed and framed, and whether these frames and sentiments change around specific events such as Brexit and the Migration wave of 2015. A good place to start answering this inquiry is by looking at the parliamentary speeches from the HoC debates because they are held by public elected members who, in principle, represent the interests and voice of their electors.Further, as the discussions in the House are instrumental to the unfolding policies and pieces of legislation regarding immigrants, understanding the views and frames voiced in the discussion may shed light on how these frames impacted resulting policy about immigration. In this sense, our text analysis could result in understanding better a treatment, which allows for induction of future research hypothesis.
explain which data has been used and forms the basis of our analysis – text analysis, what it is, text sentiment and topic and what can we get out of it.
To understand how immigrants are framed and perceived when discussed in parliamentary debates, we used a database called ParlSpeech V2 by Christian Rauh and Jan Schwalbach (2020). This database is unique in its scope, covering all parliamentary debates from 1998 and up until 2020, resulting in 1,956,223 speeches (Rauh & Schwalbach, 2020, p. 10). The text was collected from the digital Commons Hansard that contains the plenary protocols and documents from which speech texts and metadata are extracted. The corpus contains a range of covariates like party affiliation and agenda which facilitate better analysis of the various ways in which the topic of consideration is discussed by the different parties’ representatives and depending on the agenda context. For that end, we also leverage the (established to produce reliable estimates) Lexicoder 2015 sentiment dictionary that consists of 2,858-word patterns relating to negative sentiment and 1,709-word patterns, indicating positive sentiment (Young & Soroka, 2012).
explain the subset + limitations Choosing a unit for analysis is a challenging task, and in our case, the desicions we took were related both to substantive and practical consideration of needing to narrow down a very large database to perform a more in depth analysis. Thus, we choose to focus on texts from 2010 to present day. 2010 is a good starting point for our analysis because that was the year of the Tory manifesto and the general elections which resulted with a win for the Conservative party. This allow us for a sufficient time frame that has observations both before our main events of interest, namely the 2015 General Election, the migration wave and the Brexit Referendum, and after, from 2016 until 2020. In terms of content, we subset the corpus only to those speeches that contain a reference to key words related to the topic. Specifically, “immigra”, “refugee” or “asylum” because we expect parliamentary debates to be explicit in their language, meaning that if immigration is discussed one of these key words will show either in the agenda description or in the speech itself and therefore we think this method would allow us to capture most of the substantive debates regarding immigration (Van Dijk, 2000). This type of subsetting allows us to focus our analysis and remove noise from unrelated text, and yet, contain the limitation of not including any documents who discuss immigration without mentioning the three key terms chosen in either agenda description or text. Further, by this subsetting we are very likely to loose short responses to speeches carried out.
mention that we will look at two basic subsets: One general one with all obrservations of the initial subset, and one based on the context of the keywords. Justify why.
mention events etc.
2.2.1 General Corpus
2.2.3 General consideration/definitions used across analysis
Justifications and throughts here.
Subset covers 3.05% of total debates in that time period and 6.35% of the total time spend in debates.
This section looks at the overall prevalence of immigration-realted debates in the HoC between 2010 and 2020, irrespective of party.
We use a density plot that depicts frequency (y) across time.
Any reference is based on the subset and therefore immigration-related.
Plot 1 shows the number of individual contributions made over time. Technically speaking, this equals the total count of documents for each month between 2010 and 2020. Substantively, one document reflect one individual’s contribution irrespective of its length, tone etc.
Findings plot 1: Need to mention spikes and breaks (likely due to the different phases of the HoC; e.g. summer breaks)
Plot 2 depicts the amount of unique agenda points either dedicated towards or somehow relating to immigration. Substantively, this means that each agenda point, irrespective of its lengths, will be counted.
Findings plot 2: The overall amount of agenda points devoted or somehow related to immigration has almost tripled between 2010 and 2020, with a nearly linear increase over the years. ,
Plot 3. While plot 1 shows the overall count of unique contributions to immigration-related debates, it does not give substantive insights into the lengths of those contributions. We argue that looking at the overall amount of words used within debates is a relatively clear indicator of the time spent on the respective debate. This is important, as the HoC only has a limited time available, devoting more time towards a debate may indicate certain priorities. In this regard, plot 3 depicts the 6-month-average total amount of words spend on immigration-related debates. By looking at the 6-month averages, we are able to observe whether debate-preferances prevailed over time or whether they only peaked over a short time. To give you an example, looking at the number of words on Dec 2011 indicates the monthly-average amount of words spend on immigration related debates during the second half of 2011.
Findings plot 3: While sharp ups and downs were still visible in plots 1 and 2, averaging over 6 months allows for a smoother observation of debate evolution. From January 2012 to November 2014 we are able to observe a steady increase in time spend on debates with regards to their 6-month averages. This is likely due to the spikes showing on a monthly level in both January and June of 2014. The second half of 2014 as well as the first half of 2015 saw less time being devoted to immigration related debates. This suggest that overall, the content on which we selected our subset did not increase in particular prevalence before the 2015 General Election. However, Between May 2015 and June 2016, hence the year following the general election and leading up to the Brexit referendum, saw a major increase in time spend on immigration-related debates. On average, the HoC spend almost twice as much time on immigration related debates during Sep 2015 - Feb 2016 when compared to the period of Dec 2014 - May 2015. Hence, debates seem to have gained in priority after the GEneral election and leading up to the referendum.
Plot 1: Prevalence of immigration debates over time by month | Total number of words as a proxy for time spent on debating.
Concentration of party-specific contributions
This density plot gives us a sense of the frequency each party discussed each party discussed each month during the time frame of our research. Basically what it does it counts how many words each party each party invested in speaking about immigration related topics. So for example, while the SNP and the DUP spoke more about immigration after Brexit, other parties exhibit a more constant trend of engagement with immigration related speech. Importantly, the information that can be gathered from this graph is limited in that it does not tell us anything about substance of these speeches, but crudely how many words were used. Nevertheless, this descriptive visualization does help us get an initial sense about the prevelance of immigration related speech in each of the parties we are focusing on.
Sentiment | Overall Corpus
Graph 1: Overall Sentiment
Graph 2: Sentiment by party
Create KWIC - Dataframe, Corpus and Dfm
Subset KWIC according to keywords
Sentiment Keywords in Context of keyword
Graph Sentiment by keywords
Create Dfm Include here to also include sentiment in the dfm.
The topics found by the stm model are exclusive
Topic Visualizations:
Topic evolution over time by party
sentiment and Topics: Correlation
## correlations
## iraq, rohingya, libya -0.0127621260
## unaccompanied, trafficked, detention -0.0983944435
## tb, sikh, auschwitz -0.0429929832
## vote, voting, motion -0.0006179458
## eea, seasonal, visa 0.1478371098
## allowance, tax, dwp -0.0125341125
Frequency of words related to migration (including key terms)
check what the sentiment analysis does
KWIC - Wordcloud incl. keywords
KWIC - Wordcloud excl. keywords
Plot: Top words kwic co-concurrence matrix
The following code tries to investigate the relation between sentiments and topic, and how that changes by parties. This does not aim to show causal relations but to provide the reader with an intuition how sentiment regarding different topics vary between parties, and, depending on the topic discussed.
Data frames for lm models
lm models for each topic
output table for lm models
plot correlation between sentiment and topic
plot correlation between sentiment and topic by party
Topics by party in periods before the election, before and after Brexit
Average Topic prevalence over time
Insights into Topics